Recovery of biodiversity and carbon stocks in forest landscape restoration

Abstract

The forest cover in the Atlantic Forest biome is currently composed of different origins, such as natural regeneration, restoration plantations, commercial monocultures, degraded forest areas, among others. Furthermore, they have different ages, floristic composition and varied structures, having different market impacts with their diversity. Understanding the dynamics of aboveground carbon (AGC) stocks in restoring forests is essential to support international forest restoration agreements and the UN Decade of Ecological Restoration.Monitoring restored areas with different previous land uses and different restoration strategies is crucial for quantifying carbon stocks and evaluating ecosystem services, such as biodiversity, and soil and water protection. New remote sensing approaches have revolutionized the way we monitor the diversity, structure and function of forests, and can play a key role in tracking the progress of restoration programs.AGC estimates are generally performed indirectly (non-destructively) using allometric models developed in mature forests. Little is known about the allometric relationships of individuals (diameter, height and biomass of trees) and the landscape (canopy structure and forest AGB) of forests under restoration. Due to the lack of equations and studies of carbon dynamics in restored forests, it is common to use generalised equations for tropical forests for this carbon estimate, adding many uncertainties to the estimates.In this project, we will explore remote sensing technologies to generate specific equations from lidar metrics to estimate above-ground carbon in different types of restoration. From the results generated, it will be possible to quantify the uncertainties associated with the use of generalized equations to estimate carbon in restored forests, as well as the equations with lower uncertainties for each type of restoration.